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Tytuł artykułu

Accurate Frequency Estimation Based On Three-Parameter Sine-Fitting With Three FFT Samples

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Języki publikacji
EN
Abstrakty
EN
This paper presents a simple DFT-based golden section searching algorithm (DGSSA) for the single tone frequency estimation. Because of truncation and discreteness in signal samples, Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT) are inevitable to cause the spectrum leakage and fence effect which lead to a low estimation accuracy. This method can improve the estimation accuracy under conditions of a low signal-to-noise ratio (SNR) and a low resolution. This method firstly uses three FFT samples to determine the frequency searching scope, then – besides the frequency – the estimated values of amplitude, phase and dc component are obtained by minimizing the least square (LS) fitting error of three-parameter sine fitting. By setting reasonable stop conditions or the number of iterations, the accurate frequency estimation can be realized. The accuracy of this method, when applied to observed single-tone sinusoid samples corrupted by white Gaussian noise, is investigated by different methods with respect to the unbiased Cramer-Rao Low Bound (CRLB). The simulation results show that the root mean square error (RMSE) of the frequency estimation curve is consistent with the tendency of CRLB as SNR increases, even in the case of a small number of samples. The average RMSE of the frequency estimation is less than 1.5 times the CRLB with SNR = 20 dB and N = 512.
Rocznik
Strony
403--416
Opis fizyczny
Bibliogr. 25 poz., rys., wykr., wzory
Twórcy
autor
  • North University of China, National Key Laboratory for Electronic Measurement and Technology, Taiyuan, 030051, China
  • Taiyuan University of Science and Technology, College of Electronics and Information Engineering, Taiyuan 030024, China
autor
  • North University of China, National Key Laboratory for Electronic Measurement and Technology, Taiyuan, 030051, China
autor
  • North University of China, National Key Laboratory for Electronic Measurement and Technology, Taiyuan, 030051, China
autor
  • North University of China, National Key Laboratory for Electronic Measurement and Technology, Taiyuan, 030051, China
Bibliografia
  • [1] Borkowski, J. Kania, D., Mroczka, J. (2014). Interpolated-DFT-Based Fast and Accurate Frequency Estimation for the Control of Power. IEEE Transactions on industrial Electronics, 61(12), 7026-7034.
  • [2] Pantazis, D.Y., Rosec, O., Stylianou, Y. (2010). Iterative Estimation of Sinusoidal Signal Parameters. IEEE Signal Processing Letters, 17(5), 461-464.
  • [3] Robbins, PA. (1984). The ventilatory response of the human respiratory system to sine waves of alveolar carbon dioxide and hypoxia. The Journal of Physiology, 350, 461-474.
  • [4] Vanlanduit, R., Vanherzeele, J., Guillaume, P., Cauberghe, B., Verboven, P. (2004). Fourier fringe processing by use of an interpolated Fourier-transform technique. Appl. Optics., 43, 5206-5213.
  • [5] Candan, C. (2011). A Method For Fine Resolution Frequency Estimation From Three DFT Samples. IEEE Signal Processing Letters, 18(6), 351-354.
  • [6] Liao, J., Lo, S. (2014). Analytical solutions for frequency estimators by interpolation of DFT coefficients. Signal Processing, 100, 93-100.
  • [7] Aboutanios E., Ye, S. (2014), Efficient Iterative Estimation of the Parameters of a Damped Complex Exponential in Noise. IEEE Signal Processing Letters, 21(8), 975-979.
  • [8] Zieliński, T.P., Duda, K. (2011). Frequency and damping estimation method - an overview. Metrol. Meas. Syst., 18(4), 505-528.
  • [9] Duda, K., Zieliński, T.P., Magalas, L.B., Majewski, M. (2011). DFT-based Estimation of Damped Oscillation Parameters in Low-frequency Mechanical Spectroscopy. IEEE Trans. Instrum. Meas., 60(11), 3608-3618.
  • [10] Yoshida, Y.I., Sugai, T., Tani, S., Motegi, M., Minamida, K., Hayakawa, H. (1981). Automation of internal friction measurement apparatus of inverted torsion pendulum type. J. Phys. E. Sci. Instrum., 14, 1201-1206.
  • [11] Rife, D.C., Vincent, G.A. (1970). Use of the discrete Fourier transform in the measurement of frequencies and levels of tones. Bell Syst. Tech. J., 49(2), 197-228.
  • [12] Zhenmiao, D., Yu, L. (2007). The Starting Point Problem of Sinusoid Frequency Estimation Based on Newton’s Method. Acta Electronica Sinica, 35(1), 104-107.
  • [13] Xudong, W., Yu, L., Zhenmiao, D. (2008). Modified Rife algorithm for frequency estimation of sinusoid and implementation in FPGA. Systems Engineering and Electronics, 30(4), 621-624.
  • [14] Duda, K., Zieliński, T.P. (2013). Efficacy of the Frequency and Damping Estimation of a Real-Value Sinusoid Part 44 in a series of tutorials on instrumentation and measurement. IEEE Instrumentation & Measurement Magazine,16(2), 48-58.
  • [15] Steiglitz K., McBride, L.E. (1965). A technique for identification of linear systems. IEEE Trans. Automatic Control, (10), 461-464.
  • [16] Wu, R.C., Chiang, C.T. (2010). Analysis of the Exponential Signal by the Interpolated DFT Algorithm. IEEE Trans. Instrum. Meas., 59(12), 3306-3317.
  • [17] IEEE Standard for terminology and test methods for analog-to-digital converters. (2000). IEEE Std. 1241.
  • [18] Bilau, T.Z., Megyeri, T., Sarhegyi, A., Markus, J., Kollar, I. (2004). Four-parameter fitting of sine wave testing result: iteration and convergence. Computer Standards & Interfaces, 26, 51-56.
  • [19] Andersson, T., Handel, P. (2006). IEEE Standard 1057, Cramer-Rao Bound and the Parsimony Principle. Instrumentation and Measurement. IEEE Transactions On, 55(1), 44-53.
  • [20] IEEE Standard for Digitizing Waveform Recorders. (2008). IEEE Std. 1057-2007.
  • [21] da Silva, M.F., Cruz Serra, A. (2003). New methods to improve convergence of sine fitting algorithms. Computer Standards & Interfaces, 25, 23-31.
  • [22] Bilau, T.Z., Megyeri, T., Sárhegyi, A. (2002). Four parameter fitting of sine wave testing results: iteration and convergence. 4th International Conference on Advanced A/D and D/A Conversion Techniques and their Applications, and 7th European Workshop on ADC Modelling and Testing, Prague, Czech Republic, 1-5.
  • [23] Wilde, D.J., Beightler, C.S. (1967). Foundations of optimization. Englewood Cliffs. NJ: Prentice-Hall Inc.
  • [24] Wen-Chen, Y., Lun, Z., Qian, R., Meng, Z. (2013). Multi-objective Optimization for Traffic Signals with Golden Ratio Based Genetic Algorithm. Journal of Transportation Systems Engineering and Information Technology, 13(5), 48-55.
  • [25] Zhang, L., Zhang, M., Yang, W., Dong, D. (2015). Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network. Computational Intelligence and Neuroscience, 1-9.
Uwagi
EN
This work was financially supported by National Natural Science Foundation of China (No. 51275492), the International Science & Technology Cooperation Project of the Ministry of Science and Technology, People’s Republic of China (No. 2010DFB10480).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-01403ba5-f82d-413b-96cb-bc815d1362dd
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